import os

import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler

import commons
import utils
from data_utils import (TextAudioSpeakerLoader, TextAudioSpeakerCollate,
                        DistributedBucketSampler)
from models import (
    SynthesizerTrn,
    MultiPeriodDiscriminator,
)
from losses import (generator_loss, discriminator_loss, feature_loss, kl_loss)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from text.symbols import symbols

torch.backends.cudnn.benchmark = True
global_step = 0


def main():
    """Assume Single Node Multi GPUs Training Only"""
    assert torch.cuda.is_available(), "CPU training is not allowed."

    n_gpus = torch.cuda.device_count()
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '80000'

    hps = utils.get_hparams()
    mp.spawn(run, nprocs=n_gpus, args=(
        n_gpus,
        hps,
    ))


def run(rank, n_gpus, hps):
    global global_step
    if rank == 0:
        logger = utils.get_logger(hps.model_dir)
        logger.info(hps)
        utils.check_git_hash(hps.model_dir)
        writer = SummaryWriter(log_dir=hps.model_dir)
        writer_eval = SummaryWriter(
            log_dir=os.path.join(hps.model_dir, "eval"))

    dist.init_process_group(backend='nccl',
                            init_method='env://',
                            world_size=n_gpus,
                            rank=rank)
    torch.manual_seed(hps.train.seed)
    torch.cuda.set_device(rank)

    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
    train_sampler = DistributedBucketSampler(
        train_dataset,
        hps.train.batch_size, [32, 300, 400, 500, 600, 700, 800, 900, 1000],
        num_replicas=n_gpus,
        rank=rank,
        shuffle=True)
    collate_fn = TextAudioSpeakerCollate()
    train_loader = DataLoader(train_dataset,
                              num_workers=8,
                              shuffle=False,
                              pin_memory=True,
                              collate_fn=collate_fn,
                              batch_sampler=train_sampler)
    if rank == 0:
        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files,
                                              hps.data)
        eval_loader = DataLoader(eval_dataset,
                                 num_workers=8,
                                 shuffle=False,
                                 batch_size=hps.train.batch_size,
                                 pin_memory=True,
                                 drop_last=False,
                                 collate_fn=collate_fn)

    net_g = SynthesizerTrn(len(symbols),
                           hps.data.filter_length // 2 + 1,
                           hps.train.segment_size // hps.data.hop_length,
                           n_speakers=hps.data.n_speakers,
                           **hps.model).cuda(rank)
    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
    optim_g = torch.optim.AdamW(net_g.parameters(),
                                hps.train.learning_rate,
                                betas=hps.train.betas,
                                eps=hps.train.eps)
    optim_d = torch.optim.AdamW(net_d.parameters(),
                                hps.train.learning_rate,
                                betas=hps.train.betas,
                                eps=hps.train.eps)
    net_g = DDP(net_g, device_ids=[rank])
    net_d = DDP(net_d, device_ids=[rank])

    try:
        _, _, _, epoch_str = utils.load_checkpoint(
            utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
            optim_g)
        _, _, _, epoch_str = utils.load_checkpoint(
            utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
            optim_d)
        global_step = (epoch_str - 1) * len(train_loader)
    except Exception as e:
        epoch_str = 1
        global_step = 0

    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
        optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
        optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)

    scaler = GradScaler(enabled=hps.train.fp16_run)

    for epoch in range(epoch_str, hps.train.epochs + 1):
        if rank == 0:
            train_and_evaluate(rank, epoch, hps, [net_g, net_d],
                               [optim_g, optim_d], [scheduler_g, scheduler_d],
                               scaler, [train_loader, eval_loader], logger,
                               [writer, writer_eval])
        else:
            train_and_evaluate(rank, epoch, hps, [net_g, net_d],
                               [optim_g, optim_d], [scheduler_g, scheduler_d],
                               scaler, [train_loader, None], None, None)
        scheduler_g.step()
        scheduler_d.step()


def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler,
                       loaders, logger, writers):
    net_g, net_d = nets
    optim_g, optim_d = optims
    scheduler_g, scheduler_d = schedulers
    train_loader, eval_loader = loaders
    if writers is not None:
        writer, writer_eval = writers

    train_loader.batch_sampler.set_epoch(epoch)
    global global_step

    net_g.train()
    net_d.train()
    for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths,
                    speakers) in enumerate(train_loader):
        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(
            rank, non_blocking=True)
        spec, spec_lengths = spec.cuda(
            rank, non_blocking=True), spec_lengths.cuda(rank,
                                                        non_blocking=True)
        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
            rank, non_blocking=True)
        speakers = speakers.cuda(rank, non_blocking=True)

        with autocast(enabled=hps.train.fp16_run):
            y_hat, l_length, attn, ids_slice, x_mask, z_mask, (
                z, z_p, m_p, logs_p, m_q,
                logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers)

            mel = spec_to_mel_torch(spec, hps.data.filter_length,
                                    hps.data.n_mel_channels,
                                    hps.data.sampling_rate, hps.data.mel_fmin,
                                    hps.data.mel_fmax)
            y_mel = commons.slice_segments(
                mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
            y_hat_mel = mel_spectrogram_torch(
                y_hat.squeeze(1), hps.data.filter_length,
                hps.data.n_mel_channels, hps.data.sampling_rate,
                hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin,
                hps.data.mel_fmax)

            y = commons.slice_segments(y, ids_slice * hps.data.hop_length,
                                       hps.train.segment_size)  # slice

            # Discriminator
            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
            with autocast(enabled=False):
                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
                    y_d_hat_r, y_d_hat_g)
                loss_disc_all = loss_disc
        optim_d.zero_grad()
        scaler.scale(loss_disc_all).backward()
        scaler.unscale_(optim_d)
        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
        scaler.step(optim_d)

        with autocast(enabled=hps.train.fp16_run):
            # Generator
            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
            with autocast(enabled=False):
                loss_dur = torch.sum(l_length.float())
                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p,
                                  z_mask) * hps.train.c_kl

                loss_fm = feature_loss(fmap_r, fmap_g)
                loss_gen, losses_gen = generator_loss(y_d_hat_g)
                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
        optim_g.zero_grad()
        scaler.scale(loss_gen_all).backward()
        scaler.unscale_(optim_g)
        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
        scaler.step(optim_g)
        scaler.update()

        if rank == 0:
            if global_step % hps.train.log_interval == 0:
                lr = optim_g.param_groups[0]['lr']
                losses = [
                    loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl
                ]
                logger.info('Train Epoch: {} [{:.0f}%]'.format(
                    epoch, 100. * batch_idx / len(train_loader)))
                logger.info([x.item() for x in losses] + [global_step, lr])

                scalar_dict = {
                    "loss/g/total": loss_gen_all,
                    "loss/d/total": loss_disc_all,
                    "learning_rate": lr,
                    "grad_norm_d": grad_norm_d,
                    "grad_norm_g": grad_norm_g
                }
                scalar_dict.update({
                    "loss/g/fm": loss_fm,
                    "loss/g/mel": loss_mel,
                    "loss/g/dur": loss_dur,
                    "loss/g/kl": loss_kl
                })

                scalar_dict.update({
                    "loss/g/{}".format(i): v
                    for i, v in enumerate(losses_gen)
                })
                scalar_dict.update({
                    "loss/d_r/{}".format(i): v
                    for i, v in enumerate(losses_disc_r)
                })
                scalar_dict.update({
                    "loss/d_g/{}".format(i): v
                    for i, v in enumerate(losses_disc_g)
                })
                image_dict = {
                    "slice/mel_org":
                    utils.plot_spectrogram_to_numpy(
                        y_mel[0].data.cpu().numpy()),
                    "slice/mel_gen":
                    utils.plot_spectrogram_to_numpy(
                        y_hat_mel[0].data.cpu().numpy()),
                    "all/mel":
                    utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
                    "all/attn":
                    utils.plot_alignment_to_numpy(attn[0,
                                                       0].data.cpu().numpy())
                }
                utils.summarize(writer=writer,
                                global_step=global_step,
                                images=image_dict,
                                scalars=scalar_dict)

            if global_step % hps.train.eval_interval == 0:
                evaluate(hps, net_g, eval_loader, writer_eval)
                utils.save_checkpoint(
                    net_g, optim_g, hps.train.learning_rate, epoch,
                    os.path.join(hps.model_dir,
                                 "G_{}.pth".format(global_step)))
                utils.save_checkpoint(
                    net_d, optim_d, hps.train.learning_rate, epoch,
                    os.path.join(hps.model_dir,
                                 "D_{}.pth".format(global_step)))
        global_step += 1

    if rank == 0:
        logger.info('====> Epoch: {}'.format(epoch))


def evaluate(hps, generator, eval_loader, writer_eval):
    generator.eval()
    with torch.no_grad():
        for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths,
                        speakers) in enumerate(eval_loader):
            x, x_lengths = x.cuda(0), x_lengths.cuda(0)
            spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
            y, y_lengths = y.cuda(0), y_lengths.cuda(0)
            speakers = speakers.cuda(0)

            # remove else
            x = x[:1]
            x_lengths = x_lengths[:1]
            spec = spec[:1]
            spec_lengths = spec_lengths[:1]
            y = y[:1]
            y_lengths = y_lengths[:1]
            speakers = speakers[:1]
            break
        y_hat, attn, mask, *_ = generator.module.infer(x,
                                                       x_lengths,
                                                       speakers,
                                                       max_len=1000)
        y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length

        mel = spec_to_mel_torch(spec, hps.data.filter_length,
                                hps.data.n_mel_channels,
                                hps.data.sampling_rate, hps.data.mel_fmin,
                                hps.data.mel_fmax)
        y_hat_mel = mel_spectrogram_torch(
            y_hat.squeeze(1).float(), hps.data.filter_length,
            hps.data.n_mel_channels, hps.data.sampling_rate,
            hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin,
            hps.data.mel_fmax)
    image_dict = {
        "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
    }
    audio_dict = {"gen/audio": y_hat[0, :, :y_hat_lengths[0]]}
    if global_step == 0:
        image_dict.update(
            {"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
        audio_dict.update({"gt/audio": y[0, :, :y_lengths[0]]})

    utils.summarize(writer=writer_eval,
                    global_step=global_step,
                    images=image_dict,
                    audios=audio_dict,
                    audio_sampling_rate=hps.data.sampling_rate)
    generator.train()


if __name__ == "__main__":
    main()
